Spatial Analysis of COVID-19 cases and intensive care beds in the State of Ceará, Brazil

The geographical distribution of COVID-19 through Geographic Information Systems resources is hardly explored. We aimed to analyze the distribution of COVID-19 cases and the exclusive intensive care beds in the state of Ceará, Brazil. This is an ecological study with the geographic distribution of the case detection coefficient in 184 municipalities. Maps of crude and estimated values (global and local Bayesian method) were developed, calculating the Moran index and using BoxMap and MoranMap. Intensive care beds were distributed through geolocalized points. In total, 3,000 cases and 459 beds were studied. The highest rates were found in the capital Fortaleza, the Metropolitan Region (MR), and the south of this region. A positive spatial autocorrelation has been identified in the local Bayesian rate (I = 0.66). The distribution of beds superimposed on the BoxMap shows clusters with a High-High pattern of number of beds (capital, MR, northwestern part). However, a similar pattern is found in the far east or transition areas with insufficient beds. The MoranMap shows clusters statistically significant in the state. COVID-19 interiorization in Ceará requires contingency measures geared to the distribution of specific intensive care beds for COVID-19 cases in order to meet the demand.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:25

Enthalten in:

Ciencia & saude coletiva - 25(2020), suppl 1 vom: 08. Juni, Seite 2461-2468

Sprache:

Portugiesisch

Weiterer Titel:

Análise Espacial dos Casos de COVID-19 e leitos de terapia intensiva no estado do Ceará, Brasil

Beteiligte Personen:

Pedrosa, Nathália Lima [VerfasserIn]
Albuquerque, Nila Larisse Silva de [VerfasserIn]

Links:

Volltext

Themen:

Journal Article

Anmerkungen:

Date Completed 17.06.2020

Date Revised 18.12.2020

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1590/1413-81232020256.1.10952020

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM310996082